Clinically Relevant Manual One-Shot Learning Technique (MOST) to Personalize Fractional Carbon Dioxide Laser Treatment for Eyelid Scars
Bibliographic record
Abstract
BACKGROUND: Hypertrophic and keloid eyelid scars cause functional and aesthetic problems. Traditional fractional carbon dioxide (CO2) laser treatments require multiple sessions and use uniform parameters regardless of scar characteristics, limiting their effectiveness for complex eyelid scars. OBJECTIVES: In this study, the authors evaluate the efficacy and safety of the Manual One-shot Learning Technique (MOST) with fractional CO2 laser for personalized treatment of eyelid scars. METHODS: A retrospective study involved 154 patients with hypertrophic and keloid eyelid scars treated with the MOST fractional CO2 laser. Functional and aesthetic outcomes were assessed utilizing degree of incomplete eyelid closure, ectropion, and Vancouver Scar Scale (VSS). Patients were followed up for a total of 12 months to evaluate outcomes and monitor complications. RESULTS: The study included 154 patients (98 males, 56 females; mean age 32.6 ± 14.2 years). A single treatment session resulted in significant improvements, with a mean VSS score reduction of 2.75 ± 1.50 (P = .021), and 57.8% of patients achieving >75% scar clearance. After 1 to 3 treatment sessions, all patients showed significant functional and aesthetic improvements, with incomplete eyelid closure and ectropion significantly reduced (both P < .001), VSS scores decreased by 3.02 ± 1.76 (P < .001), and 90.9% of patients achieved >75% scar clearance. Complications included recurrence (10.4%), hypopigmentation (5.2%), and skin atrophy (5.2%). CONCLUSIONS: The MOST laser technique significantly improves functional and aesthetic outcomes for eyelid scars with fewer treatment sessions. By personalizing treatment based on tissue response, this approach enhances efficiency while reducing both patient burden and resource use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".